SoroushVahidi/scidocs huggingface dataset:
Web science and human-computer interaction: when disciplines collide
(2012)

Web Science and Human-Computer Interaction (HCI) are interdisciplinary arenas concerned with the intersection of people and technology. After introducing the two disciplines we discuss overlaps and notable differences between them, covering subject matter, scope and methodology. Given the longer history of HCI, we identify and discuss some potential lessons that the Web Science community may be able to take from this field. These concern: the division between interpretivist and positivist approaches; methods and methodology; evaluation; and design focus and methods. In summary, this paper clarifies the relationship between the communities, signposting complementary aspects and ways in which they might collaborate in future.
Summarization Evaluation Methods: Experiments and Analysis
(1998)

Two methods are used for evaluation of summarization systems: an evaluation of generated summaries against an "ideal" summary and evaluation of how well summaries help a person perform in a task such as informa. tion retrieval. We carried out two large experiments to study the two evaluation methods. Our results show that different parameters of an experiment can (h-amatically affect how well a system scores. For example, summary length was found to affect both types of evaluations. For the "ideal" summary based evaluation, accuracy decreases as summary length increases, while for task based evaluations summary length and accuracy on an information retrieval task appear to correlate randomly. In this paper, we show how this parameter and others can affect evaluation results and describe how parameters can be controlled to produce a sound evaluation.
The 2 nd Workshop on Arabic Corpora and Processing Tools 2016 Theme :
(2016)

Speech datasets and corpora are crucial for both developing and evaluating accurate Natural Language Processing systems. While Modern Standard Arabic has received more attention, dialects are drastically underestimated, even they are the most used in our daily life and the social media, recently. In this paper, we present the methodology of building an Arabic Speech Corpus for Algerian dialects, and the preliminary version of that dataset of dialectal arabic speeches uttered by Algerian native speakers selected from different Algeria’s departments. In fact, by means of a direct recording way, we have taken into acount numerous aspects that foster the richness of the corpus and that provide a representation of phonetic, prosodic and orthographic varieties of Algerian dialects. Among these considerations, we have designed a rich speech topics and content. The annotations provided are some useful information related to the speakers, time-aligned orthographic word transcription. Many potential uses can be considered such as speaker/dialect identification and computational linguistic for Algerian sub-dialects. In its preliminary version, our corpus encompasses 17 sub-dialects with 109 speakers and more than 6 K utterances.
Bike Angels: An Analysis of Citi Bike's Incentive Program
(2018)

Bike-sharing systems provide a sustainable and affordable transportation alternative in many American cities. However, they also face intricate challenges due to imbalance, caused by asymmetric traffic demand. That imbalance often-times leads to bike-sharing stations being empty (full), causing out-of-stock events for customers that want to rent (return) bikes at such stations. In recent years, the study of data-driven methods to help support the operation of such system, has developed as a popular research area.
 In this paper, we study the impact of Bike Angels, an incentive program New York City's Citi Bike system set up in 2015 to crowd-source some of its operational challenges related to imbalance. We develop a performance metric for both online- and offline-policies to set incentives within the system; our results indicate that though Citi Bike's original offline policy performed well in a regime in which incentives given to customers are not associated to costs, there is ample space for improvement when the costs of the incentives are taken into consideration. Motivated by these findings, we develop several online- and offline- policies to investigate the trade-offs between real-time and offline decision-making; one of our online policies has since been adopted by Citi Bike.
Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies
(2015)

Transportation is a necessary infrastructure for our modern society. The performance of transportation systems is of crucial importance for individual mobility, commerce, and for the economic growth of all nations. In recent years modern society has been facing more traffic jams, higher fuel prices, and an increase in CO2 emissions. It is imperative to improve the safety and efficiency of transportation. Developing a sustainable intelligent transportation system requires the seamless integration and interoperability with emerging technologies such as connected vehicles, cloud computing, and the Internet of Things. In this article we present and discuss some of the integration challenges that must be addressed to enable an intelligent transportation system to address issues facing the transportation sector such as high fuel prices, high levels of CO2 emissions, increasing traffic congestion, and improved road safety.
A Deep Dual-Branch Networks for Joint Blind Motion Deblurring and Super-Resolution
(2018)

Image super-resolution is a fundamental pre-processing technique for the machine vision applications of robotics and other mobile platforms. Inevitably, images captured by the mobile camera tend to emerge severe motion blur and this degradation will deteriorate the performance of current state-of-the-art super-resolution methods. In this paper, we propose a deep dual-branch convolution neural network (CNN) to generate a clear high-resolution image from a single natural image with severe blurs. Compared to off-the-shelf methods, our method, called DB-SRN, can remove the complex non-uniform motion blurs and restore useful texture details simultaneously. By sharing the features from modified residual blocks (ResBlocks), the dual-branch design can promote the performances of both tasks other while retaining network simplicity. Extensive experiments demonstrate that our method produces remarkable deblurred and super-resolved images in terms of quality and quantity with high computational efficiency.
Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments
(2012)

With the increase of energy consumption associated with IT infrastructures, energy management is becoming a priority in the design and operation of complex service-based systems. At the same time, service providers need to comply with Service Level Agreement (SLA) contracts which determine the revenues and penalties on the basis of the achieved performance level. This paper focuses on the resource allocation problem in multitier virtualized systems with the goal of maximizing the SLAs revenue while minimizing energy costs. The main novelty of our approach is to address-in a unifying framework-service centers resource management by exploiting as actuation mechanisms allocation of virtual machines (VMs) to servers, load balancing, capacity allocation, server power state tuning, and dynamic voltage/frequency scaling. Resource management is modeled as an NP-hard mixed integer nonlinear programming problem, and solved by a local search procedure. To validate its effectiveness, the proposed model is compared to top-performing state-of-the-art techniques. The evaluation is based on simulation and on real experiments performed in a prototype environment. Synthetic as well as realistic workloads and a number of different scenarios of interest are considered. Results show that we are able to yield significant revenue gains for the provider when compared to alternative methods (up to 45 percent). Moreover, solutions are robust to service time and workload variations.
Extraction of Significant Patterns from Heart Disease Warehouses for Heart Attack Prediction
(2009)

The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various factors or symptoms is a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. The healthcare industry gathers enormous amounts of heart disease data that regrettably, are not “mined” to determine concealed information for effective decision making by healthcare practitioners. In this paper, we have proposed an efficient approach for the extraction of significant patterns from the heart disease warehouses for heart attack prediction. Initially, the data warehouse is preprocessed to make it appropriate for the mining process. After preprocessing, the heart disease warehouse is clustered using the K-means clustering algorithm, which will extract the data relevant to heart attack from the warehouse. Subsequently the frequent patterns are mined from the extracted data, relevant to heart disease, using the MAFIA algorithm. Then the significant weightage of the frequent patterns are calculated. Further, the patterns significant to heart attack prediction are chosen based on the calculated significant weightage. These significant patterns can be used in the development of heart attack prediction system.
A survey of decision tree classifier methodology
(1991)

Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition, to name only a few. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. This paper presents a survey of current methods for DTC designs and the various existing issues. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed_ Finally, several remarks are made concerning possible future research directions.
Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease
(2007)

The prediction of survival of Coronary heart disease (CHD) has been a challenging research problem for medical society. The goal of this paper is to develop data mining algorithms for predicting survival of CHD patients based on 1000 cases .We carry out a clinical observation and a 6-month follow up to include 1000 CHD cases. The survival information of each case is obtained via follow up. Based on the data, we employed three popular data mining algorithms to develop the prediction models using the 502 cases. We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results indicated that the SVM is the best predictor with 92.1 % accuracy on the holdout sample artificial neural networks came out to be the second with91.0% accuracy and the decision trees models came out to be the worst of the three with 89.6% accuracy. The comparative study of multiple prediction models for survival of CHD patients along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data.
Selected techniques for data mining in medicine
(1999)

Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis to be coupled with methods for efficient computer-assisted analysis. This paper presents selected data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases (derivation of symbolic rules, use of background knowledge, sensitivity and specificity of induced descriptions). The importance of the interpretability of results of data analysis is discussed and illustrated on selected medical applications.
Domainless Adaptation by Constrained Decoding on a Schema Lattice
(2016)

In many applications such as personal digital assistants, there is a constant need for new domains to increase the system’s coverage of user queries. A conventional approach is to learn a separate model every time a new domain is introduced. This approach is slow, inefficient, and a bottleneck for scaling to a large number of domains. In this paper, we introduce a framework that allows us to have a single model that can handle all domains: including unknown domains that may be created in the future as long as they are covered in the master schema. The key idea is to remove the need for distinguishing domains by explicitly predicting the schema of queries. Given permitted schema of a query, we perform constrained decoding on a lattice of slot sequences allowed under the schema. The proposed model achieves competitive and often superior performance over the conventional model trained separately per domain.
Some Basic Cryptographic Requirements for Chaos-Based Cryptosystems
(2006)

In recent years, a large amount of work on chaos-based cryptosystems have been published. However many of the proposed schemes fail to explain or do not possess a number of features that are fundamentally important to all kind of cryptosystems. As a result, many proposed systems are difficult to implement in practice with a reasonable degree of security. Likewise, they are seldom accompanied by a thorough security analysis. Consequently, it is difficult for other researchers and end users to evaluate their security and performance. This work is intended to provide a common framework of basic guidelines that, if followed, every new cryptosystem would benefit from. The suggested guidelines address three main issues: implementation, key management, and security analysis, aiming at assisting designers of new cryptosystems to present their work in a more systematic and rigorous way to fulfill some basic cryptographic requirements. Meanwhile, several recommendations are made regarding some practical aspects of analog chaos-based secure communications, such as channel noise, limited bandwith, and attenuation.
A Framework for Cloud Forensic Readiness in Organizations
(2017)

Many have argued that cloud computing is one of the fastest growing and most transformative technologies in the history of computing. It has radically changed the way in which information technologies can manage, access, deliver and create services. It has also brought numerous benefits to end-users and organizations. However, this rapid growth in cloud computing adoption has also seen it become a new arena for cybercrime. This has, in turn, led to new technical, legal and organizational challenges. In addition to the large number of attacks which affect cloud computing and the decentralized nature of data processing in the cloud, many concerns have been raised. One of these concerns is how to conduct a proper digital investigation in cloud environments and be ready to collect data proactively before an incident occurs in order to save time, money and effort. This paper proposes the technical, legal and organizational factors that influence digital forensic readiness for Infrastructure as a Service consumers.
Scalable Formal Concept Analysis algorithm for large datasets using Spark
(2018)

In the process of knowledge discovery and representation in large datasets using formal concept analysis, complexity plays a major role in identifying all the formal concepts and constructing the concept lattice(digraph of the concepts). For identifying the formal concepts and constructing the digraph from the identified concepts in very large datasets, various distributed algorithms are available in the literature. However, the existing distributed algorithms are not very well suitable for concept generation because it is an iterative process. The existing algorithms are implemented using distributed frameworks like MapReduce and Open MP, these frameworks are not appropriate for iterative applications. Hence, in this paper we proposed efficient distributed algorithms for both formal concept generation and concept lattice digraph construction in large formal contexts using Apache Spark. Various performance metrics are considered for the evaluation of the proposed work, the results of the evaluation proves that the proposed algorithms are efficient for concept generation and lattice graph construction in comparison with the existing algorithms.
Mass-media coverage , its influence on public awareness of climate-change issues , and implications for Japan ’ s national campaign to reduce greenhouse gas emissions
(2009)

We analyse Japanese newspaper coverage of global warming from January 1998 to July 2007 and how public opinion during parts of that period were influenced by newspaper coverage. We show that a dramatic increase in newspaper coverage of global warming from January 2007 correlated with an increase in public concern for the issue. Before January 2007, we find that coverage of global warming had an immediate but short-term influence on public concern. With such transitory high levels of media coverage we suggest that for more effective communication of climate change, strategies aimed at maintaining mass-media coverage of global warming are required. 2008 Elsevier Ltd. All rights reserved.
mmWave communications for 5G: implementation challenges and advances
(2017)

The requirement of the fifth generation (5G) wireless communication for high throughput motivates the wireless industry to use the mmWave (millimeter wave) communications for its wide bandwidth advantage. To compensate the heavy path loss and increase the communications capacity, phased array beamforming and massive multiple-input multiple-output (MIMO) techniques are employed at both the user equipment (UE) and base stations (BS). Considering the commercial requirements, 5G mmWave large array systems should be implemented in an energy- and cost-efficient way with a small form factor. To address above issues and realize a reliable communications link, taking into account the particular characteristics of 5G mmWave systems, this paper firstly examines the design challenges and trade-offs in system implementations, then some of the design strategies are summarized. At last, recent advance in RF front-end circuits and receiver sub-systems is then highlighted.
A 6 Vpp, 52 dB, 30-dB Dynamic Range, 43 Gb/s InP DHBT Differential Limiting Amplifier
(2011)

We report the design, fabrication and measurement of a 43 Gb/s differential limiting amplifier integrating a transimpedance amplifier (TIA) as input stage and a distributed amplifier (Driver) as output stage, realised in a 1.5-µm indium phosphide (InP) double heterojunction bipolar transistor (DHBT) technology. With a differential gain higher than 52 dB, the TIA-Driver provides a constant 6 Vpp differential output amplitude for an input amplitude ranging from 12.7 mVpp to 405 mVpp, achieving more than 30 dB of electrical dynamic range, for a power consumption of 2.7 W. Designed to be used between a photodiode and an electro-optical modulator, this circuit is well suited for optical-to-electrical-to-optical (O-E-O) wavelength converters at up to 43 Gb/s.
Stress Detection and Management: A Survey of Wearable Smart Health Devices
(2017)

In today's world, many people feel stressed out from school, work, or other life events. Therefore, it is important to detect stress and manage it to reduce the risk of damage to an individual's well being. With the emergence of the Internet of Things (IoT), devices can be made to detect stress and manage it effectively by using cloudbased services and smartphone apps to aggregate and compute large data sets that track stress behavior over long periods of time. Additionally, there is added convenience via the connectivity and portability of these IoT devices. They allow individuals to seek intervention prior to elevated health risks and achieve a less stressful life.
Personal Archive Service System using Blockchain Technology: Case Study, Promising and Challenging
(2017)

This paper is to explore applications of the blockchain technology to the concept of “proof of X” such as proof of identity, proof of property ownership, proof of specific transaction, proof of college degree, proof of medical records, proof of academic achievements, etc. It describes a novel approach of building a decentralized transparent immutable secure personal archive management and service system. Personal archive is defined as a collection of various artifacts that reflect personal portfolio as well as personal unique identifications. Personal portfolio is beyond of a statement of personal achievement. It is an evidentiary document designed to provide qualitative and quantitative chronically documents and examples. Subjects can tag their information with proof, that is, certified by trusted entities or organizations like universities. Such proofs are associated with confidentiality levels exposed in the public domains. Personal identifications include biometrics as well as other multi-factors such as something the subject “has”, the subject “knows” or the subject “acts”. Stack holders in a consortium oriented blockchain network serve as verifiers and /or miners that provide their trusted services the delegated proof of stake. Such personal archive based system can be exploited to various applications including professional network like Linkedin, instant credit approval like alipay or live human from social bots like internet social media. A prototype simulation shows that such personal portfolio management and service system is feasible and immune to many ID attacks.
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
(2015)

Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief Networks (DBNs) represent the state-of-the-art for many machine learning and computer vision classification problems. To overcome the large computational cost of deep networks, spiking deep networks have recently been proposed, given the specialized hardware now available for spiking neural networks (SNNs). However, this has come at the cost of performance losses due to the conversion from analog neural networks (ANNs) without a notion of time, to sparsely firing, event-driven SNNs. Here we analyze the effects of converting deep ANNs into SNNs with respect to the choice of parameters for spiking neurons such as firing rates and thresholds. We present a set of optimization techniques to minimize performance loss in the conversion process for ConvNets and fully connected deep networks. These techniques yield networks that outperform all previous SNNs on the MNIST database to date, and many networks here are close to maximum performance after only 20 ms of simulated time. The techniques include using rectified linear units (ReLUs) with zero bias during training, and using a new weight normalization method to help regulate firing rates. Our method for converting an ANN into an SNN enables low-latency classification with high accuracies already after the first output spike, and compared with previous SNN approaches it yields improved performance without increased training time. The presented analysis and optimization techniques boost the value of spiking deep networks as an attractive framework for neuromorphic computing platforms aimed at fast and efficient pattern recognition.